Rheumatoid diabetes insulin resistance monitoring and intervention method

Abstract

The invention is applicable to the technical field of medical treatment, and provides a rheumatoid diabetes insulin resistance monitoring and intervention method, which comprises the following steps: collecting multi-dimensional data information including rheumatoid arthritis indexes, glucose metabolism parameters and hormone medication parameters; constructing an inflammation-insulin resistance index IRI, wherein IRI = (FBG * FINS) / correction factor * (1 + alpha * CRP / 10) * exp (beta * GCeq / adjustment parameter); determining a monitoring frequency and a monitoring scheme according to the inflammation-insulin resistance index range; and determining an intervention strategy according to the inflammation-insulin resistance index, the DAS28 score, the CRP, the GCeq and the FBG. According to the method, the inflammation-insulin resistance index (IRI) is constructed, metabolic parameters (FBG * FINS), inflammatory factors (CRP) and drug factors (GCeq) are incorporated into a unified mathematical model, quantitative fusion of three-dimensional pathological information is achieved, and the monitoring and intervention effects are good.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method for monitoring and intervening in insulin resistance in rheumatoid diabetes. Background Art

[0002] Rheumatoid arthritis (RA) and type 2 diabetes mellitus (T2DM) are both chronic systemic inflammatory diseases, with complex bidirectional pathological associations. Due to the long-term release of inflammatory factors (such as IL-6 and TNF-α) and glucocorticoid (GC) treatment, RA patients can experience a decrease in insulin sensitivity of approximately 30%-50%, and their diabetes incidence is 2-3 times higher than that of the general population. Existing monitoring and intervention systems have several shortcomings: traditional insulin resistance assessments (such as HOMA-IR) are based solely on blood glucose (FBG) and insulin (FINS) levels and do not integrate the additive effects of RA activity indicators (DAS28 score, CRP) and therapeutic drugs (GC equivalent dose) on metabolism. Furthermore, the inflammatory cascade during active RA and adjustments in GC dose can significantly alter insulin sensitivity, but dynamic monitoring tools that respond to these changes in real time are lacking in clinical practice. Therefore, there is a need to provide a method for monitoring and intervening in insulin resistance in rheumatoid diabetes to address the above issues. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for monitoring and intervening in insulin resistance in rheumatoid diabetes to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is achieved by providing a method for monitoring and intervening in insulin resistance in rheumatoid diabetes, the method comprising the following steps:

[0005] Collecting multi-dimensional data information, including rheumatoid arthritis indicators, glucose metabolism parameters, and hormone medication parameters;

[0006] The inflammation-insulin resistance index (IRI) was constructed as follows: IRI = (FBG × FINS) / correction factor × (1 + α × CRP / 10) × exp(β × GC eq / adjustment parameter), where FBG is fasting blood glucose, FINS is fasting insulin, α and β are constant coefficients, CRP is C-reactive protein, and GCeq is the hormone equivalent dose.

[0007] Determine monitoring frequency and schedule based on the range of the inflammation-insulin resistance index;

[0008] The intervention strategy was determined based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq, and FBG.

[0009] As a further solution of the present invention: the correction factor is obtained by fitting experimental data, and the specific steps are: obtaining the actual value of insulin resistance through an insulin clamp test; constructing a regression model, actual insulin resistance = a×(FBG×FINS)+b, and the coefficient a obtained by solving is used as the correction factor.

[0010] As a further solution of the present invention: the adjustment parameters are obtained through clinical trial analysis, and the specific steps are: designing different hormone dosage groups; and determining the adjustment parameters by determining the functional relationship between GCeq and insulin resistance changes through variance analysis or regression analysis.

[0011] As a further solution of the present invention: the step of collecting multi-dimensional data information specifically includes:

[0012] Rheumatoid arthritis indicators were collected, including CRP, DAS28 score and ESR. The DAS28 score is a disease activity score that combines joint tenderness, swelling, and CRP, and the ESR is erythrocyte sedimentation rate.

[0013] Glucose metabolism parameters were collected, including FBG, FINS, and HbA1c, where HbA1c is glycated hemoglobin;

[0014] Hormone medication parameters were collected, including GCeq, medication cycle, and biologic concentration.

[0015] As a further solution of the present invention: the α is the inflammation regulation coefficient, which is 0.35 and is used to reflect the increase in IRI when CRP increases by 10 mg / L; the β is the hormone sensitivity coefficient, which is 0.18 and is used to quantify the exponential effect of GCeq increasing by 5 mg / d.

[0016] As a further embodiment of the present invention, the step of determining the monitoring frequency and monitoring plan based on the range of the inflammation-insulin resistance index specifically includes:

[0017] Determine the range of the inflammation-insulin resistance index IRI;

[0018] When the IRI is less than the lower limit of the index, the monitoring frequency is determined to be quarterly monitoring, and the monitoring items are FBG, CRP and GCeq;

[0019] When the IRI is between the lower limit and the upper limit of the index, the monitoring frequency is determined to be biweekly dynamic monitoring, and the monitoring items are continuous blood glucose monitoring, hs-CRP, and FINS;

[0020] When the IRI is greater than the upper limit of the index, the monitoring frequency is determined to be daily, and the monitoring items are muscle biopsy, joint and pancreatic inflammation imaging.

[0021] As a further embodiment of the present invention, the step of determining the intervention strategy based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq and FBG specifically includes:

[0022] The inflammation-insulin resistance index and DAS28 score were used to determine whether the patient was in a critical metabolic-inflammatory comorbidity state;

[0023] The inflammation-insulin resistance index and CRP were used to determine whether the patient was in a state of ultra-high inflammation-driven resistance.

[0024] Determine whether the patient is in a state of hormone-induced metabolic disorder based on GCeq and FBG;

[0025] The physiological state is obtained according to the determination result, and the intervention strategy is determined according to the physiological state.

[0026] As a further solution of the present invention: the step of determining the intervention strategy according to the physiological state specifically includes:

[0027] When the physiological state is a critical metabolic-inflammatory comorbidity state, the intervention strategy is: upgrading biological agents and SGLT2 inhibitor combination strategy;

[0028] When the resistance state is driven by ultra-high inflammation, the intervention strategies are: double plasma exchange, JAK inhibitor switching, and GLP-1 receptor agonist enhancement;

[0029] When the patient is in a state of hormone-induced metabolic disorder, the intervention strategy is: optimization of metformin sustained-release and dynamic insulin sensitivity compensation.

[0030] As a further scheme of the present invention: when IRI>4.5 and DAS28 score>5.1, it is determined to be in a critical metabolic-inflammatory comorbidity state; when CRP>20mg / L and IRI>3.2, it is determined to be in an ultra-high inflammation-driven resistance state; when GCeq>7.5mg / d and FBG fluctuation>3mmol / L, it is determined to be in a hormone-induced metabolic disorder state.

[0031] As a further solution of the present invention: the multi-dimensional data information needs to be preprocessed, and the preprocessing includes data standardization, missing value processing and outlier processing.

[0032] Another object of the present invention is to provide a rheumatoid diabetes insulin resistance monitoring and intervention system, the system comprising:

[0033] A multi-dimensional data acquisition module, for collecting multi-dimensional data information, wherein the multi-dimensional data information includes rheumatoid arthritis indicators, glucose metabolism parameters and hormone medication parameters;

[0034] The resistance index construction module is used to construct the inflammation-insulin resistance index IRI, IRI = (FBG × FINS) / correction factor × (1 + α × CRP / 10) × exp(β × GCeq / adjustment parameter), where FBG is fasting blood glucose, FINS is fasting insulin, α and β are constant coefficients, CRP is C-reactive protein, and GCeq is the hormone equivalent dose;

[0035] A monitoring program determination module, used to determine the monitoring frequency and monitoring program according to the range of the inflammation-insulin resistance index;

[0036] The intervention strategy determination module is used to determine the intervention strategy based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq and FBG.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This method constructs an inflammation-insulin resistance index (IRI), incorporating metabolic parameters (FBG×FINS), inflammatory factors (CRP), and drug factors (GCeq) into a unified mathematical model to achieve quantitative integration of three-dimensional pathological information. By introducing correction factors and adjusting parameters, the model dynamically adapts to the physiological characteristics and treatment responses of different patients. Monitoring frequency and schedule are determined based on the range of the IRI, enabling dynamic monitoring and simultaneous tracking of inflammatory activity and metabolic status, resulting in more effective interventions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for monitoring and intervening in insulin resistance in rheumatoid diabetes.

[0040] Figure 2 This is a flow chart for collecting multi-dimensional data information in a method for monitoring and intervening in insulin resistance in rheumatoid diabetes.

[0041] Figure 3 A flowchart for determining the monitoring plan in a method for monitoring and intervention of insulin resistance in rheumatoid diabetes.

[0042] Figure 4 Flowchart for determining intervention strategies in an approach to monitoring and intervening with insulin resistance in rheumatoid diabetes.

[0043] Figure 5 This is a structural diagram of a rheumatoid diabetes insulin resistance monitoring and intervention system. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0045] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0046] like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring and intervening in insulin resistance in rheumatoid diabetes, the method comprising the following steps:

[0047] S100, collecting multi-dimensional data information, wherein the multi-dimensional data information includes rheumatoid arthritis indicators, glucose metabolism parameters, and hormone medication parameters;

[0048] S200, construct inflammation-insulin resistance index IRI, IRI = (FBG × FINS) / correction factor × (1 + α × CRP / 10) × ex p (β × GCeq / adjustment parameter);

[0049] S300, determine monitoring frequency and monitoring plan based on the inflammation-insulin resistance index range;

[0050] S400, intervention strategy determined based on inflammation-insulin resistance index, DAS28 score, CRP, GCeq, and FBG.

[0051] It should be noted that the existing rheumatoid diabetes insulin resistance monitoring and intervention system has the following limitations: 1. Single evaluation dimension: Traditional insulin resistance assessment (such as HOMA-IR) is only based on blood glucose (FBG) and insulin (FINS) levels, and does not integrate the cumulative effects of RA activity indicators (DAS28 score, CRP) and therapeutic drugs (GC equivalent dose) on metabolism. 2. Insufficient dynamic adaptability: The inflammatory cascade reaction and GC dose adjustment during the active phase of RA will significantly change insulin sensitivity, but there is a lack of dynamic monitoring tools that can respond to these changes in real time in clinical practice. 3. Lagging intervention strategies: Existing schemes are mostly based on fixed thresholds (such as FBG>7mmol / L to initiate intervention), and a multi-parameter linkage intervention mechanism of inflammation-metabolism-drugs has not been established, resulting in treatment adjustments lagging behind pathophysiological changes. The embodiments of the present invention are intended to solve the above problems.

[0052] In an embodiment of the present invention, multidimensional data information is first collected, and the multidimensional data information is used to construct an inflammation-insulin resistance index IRI. The multidimensional data information includes rheumatoid arthritis indicators, glucose metabolism parameters, and hormone medication parameters. The collected multidimensional data information needs to be preprocessed, and the preprocessing includes data standardization, missing value processing, and outlier processing. After data preprocessing, the inflammation-insulin resistance index IRI can be constructed, IRI = (FBG × FINS) / correction factor × (1 + α × CRP / 10) × exp (β × GCeq / adjustment parameter), where FBG is fasting blood glucose, reflecting the basal blood glucose level, in mmol / L, FINS is fasting insulin, reflecting the basal insulin secretion, in μ U / mL, α and β are constant coefficients, CRP is C-reactive protein (inflammatory marker), in mg / L, GCeq is the equivalent dose of glucocorticoid, in mg / d. The correction factor is obtained by fitting experimental data. The specific steps are: obtaining the actual value of insulin resistance through an insulin clamp test; constructing a regression model: actual insulin resistance = a × (FBG × FINS) + b; solving for the coefficient a as the correction factor. The adjustment parameter is obtained through clinical trial analysis. The specific steps are: designing different hormone dose groups (such as low-dose, medium-dose, and high-dose GCeq); and determining the functional relationship between GCeq and changes in insulin resistance through analysis of variance or regression analysis to determine the adjustment parameter. In addition, α is the inflammation regulation coefficient, which is set to 0.35 and is used to reflect the increase in IRI for every 10 mg / L increase in CRP; β is the hormone sensitivity coefficient, which is set to 0.18 and is used to quantify the exponential effect of every 5 mg / day increase in GCeq. This embodiment of the present invention constructs an inflammation-insulin resistance index (IRI), incorporating metabolic parameters (FBG × FINS), inflammatory factors (CRP), and drug factors (GCeq) into a unified mathematical model, achieving quantitative integration of three-dimensional pathological information. By introducing correction factors and adjustment parameters, the model can dynamically adapt to the physiological characteristics and treatment responses of different patients. The present invention then determines the monitoring frequency and protocol based on the range of the inflammation-insulin resistance index, enabling dynamic monitoring and simultaneous tracking of inflammatory activity and metabolic status. Finally, intervention strategies are determined based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq, and FBG, establishing a multi-parameter intervention mechanism that integrates inflammation, metabolism, and medication, resulting in more effective interventions.

[0053] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of collecting multi-dimensional data information specifically includes:

[0054] S101, collection of rheumatoid arthritis indicators, including CRP, DAS28 score, and ESR;

[0055] S102, collection of glucose metabolism parameters, including FBG, FINS, and HbA1c;

[0056] S103, collect hormone medication parameters, including GCeq, medication cycle and biological agent concentration.

[0057] In the embodiment of the present invention, rheumatoid arthritis indicators are first collected, including CRP, DAS28 score and ESR. CRP adopts ultrasensitive detection (hs-CRP) and the unit is mg / L; DAS28 score is a disease activity score that integrates joint tenderness, swelling number and CRP. ESR is erythrocyte sedimentation rate, which is used to dynamically monitor the activity of inflammation and the unit is mm / h. Then, glucose metabolism parameters are collected, including FBG, FINS and HbA1c. Among them, FBG (fasting blood glucose) requires strict morning venous blood testing and the unit is mmol / L; FINS (fasting insulin) is measured by chemiluminescence method and the unit is μIU / mL; HbA1c (glycated hemoglobin) is measured by high pressure liquid chromatography and the unit is %. Finally, hormone medication parameters are collected, including GCeq, medication cycle and biological agent concentration. GCeq (glucocorticoid equivalent dose) is converted to prednisone equivalents and is expressed in mg / day. The medication cycle refers to the number of consecutive weeks of use (to counteract the downregulation effect of GC receptors). The concentration of biologic agents, such as the trough concentration of TNF-α inhibitors, is expressed in μg / mL.

[0058] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of determining the monitoring frequency and monitoring scheme according to the inflammation-insulin resistance index range specifically includes:

[0059] S301, determining the range of the inflammation-insulin resistance index IRI;

[0060] S302, when the IRI is less than the lower limit of the index, the monitoring frequency is determined to be quarterly monitoring, and the monitoring items are FBG, CRP and GCeq;

[0061] S303, when the IRI is between the lower limit and the upper limit of the index, determining the monitoring frequency to be biweekly dynamic monitoring, and the monitoring items to be continuous blood glucose monitoring, hs-CRP, and FINS;

[0062] S304, when the IRI is greater than the upper limit of the index, the monitoring frequency is determined to be daily monitoring, and the monitoring items are muscle biopsy, joint and pancreatic inflammation imaging.

[0063] In an embodiment of the present invention, the range of the inflammation-insulin resistance index (IRI) is determined. When the IRI is less than 2.8, the monitoring frequency is determined to be quarterly routine monitoring, and the monitoring items are FBG, CRP, and GCeq. When the IRI is between 2.8 and 4.5, the monitoring frequency is determined to be biweekly dynamic monitoring, and the monitoring items are continuous blood glucose monitoring, hs-CRP, and FINS. When the IRI is greater than 4.5, the monitoring frequency is determined to be daily monitoring, and inpatient intensive monitoring is recommended. The monitoring items are muscle biopsy, joint and pancreatic inflammation imaging.

[0064] like Figure 4 As shown in FIG, as a preferred embodiment of the present invention, the step of determining the intervention strategy based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq and FBG specifically includes:

[0065] S401, determine whether the patient is in a critical metabolic-inflammatory comorbidity state based on the inflammation-insulin resistance index and DAS28 score;

[0066] S402, determining whether the patient is in a state of hyper-inflammation-driven resistance based on the inflammation-insulin resistance index and CRP;

[0067] S403, determining whether the patient is in a hormone-induced metabolic disorder state based on GCeq and FBG;

[0068] S404: Obtain the physiological state according to the determination result, and determine the intervention strategy according to the physiological state.

[0069] In the present embodiment, when IRI > 4.5 and DAS28 score > 5.1, a critical metabolic-inflammatory comorbidity state is determined; when CRP > 20 mg / L and IRI > 3.2, a hyper-inflammatory-driven resistance state is determined; and when GCeq > 7.5 mg / d and FBG fluctuation > 3 mmol / L, a hormone-induced metabolic disorder state is determined. An intervention strategy is then determined based on the physiological state obtained above. Specifically, when the physiological state is a critical metabolic-inflammatory comorbidity state, the intervention strategy is to upgrade biologics (using an IL-6 receptor antagonist (tocilizumab) 8 mg / kg intravenous injection every 4 weeks) and adopt a combination strategy of SGLT2 inhibitors. When the physiological state is a hyper-inflammatory-driven resistance state, the intervention strategy is to double plasma exchange (1.5 times the plasma volume per treatment), switch to a JAK inhibitor (preferably upadacitinib 15 mg / d), and enhance GLP-1 receptor agonist therapy (using a weekly formulation of semaglutide). When the patient is in a state of hormone-induced metabolic disorder, the intervention strategy is: optimization of metformin sustained-release and dynamic insulin sensitivity compensation (using pioglitazone 15 mg / d).

[0070] like Figure 5As shown, an embodiment of the present invention further provides a rheumatoid diabetes insulin resistance monitoring and intervention system, the system comprising:

[0071] A multi-dimensional data acquisition module 100 is used to acquire multi-dimensional data information, wherein the multi-dimensional data information includes rheumatoid arthritis indicators, glucose metabolism parameters, and hormone medication parameters;

[0072] The resistance index construction module 200 is used to construct an inflammation-insulin resistance index IRI, IRI = (FBG × FINS) / correction factor × (1 + α × CRP / 10) × exp (β × GCeq / adjustment parameter), where FBG is fasting blood glucose, FINS is fasting insulin, α and β are constant coefficients, CRP is C-reactive protein, and GCeq is the hormone equivalent dose;

[0073] A monitoring scheme determination module 300 is used to determine the monitoring frequency and monitoring scheme according to the range of the inflammation-insulin resistance index;

[0074] The intervention strategy determination module 400 is used to determine the intervention strategy according to the inflammation-insulin resistance index, DAS28 score, CRP, GCeq and FBG.

[0075] As a preferred embodiment of the present invention, the multi-dimensional data acquisition module 100 includes:

[0076] RA index collection unit, used to collect rheumatoid arthritis indicators, including CRP, DAS28 score and ESR. The DAS28 score is a disease activity score that integrates joint tenderness, swelling number and CRP, and ESR is erythrocyte sedimentation rate;

[0077] A glucose metabolism parameter collection unit is used to collect glucose metabolism parameters, including FBG, FINS and HbA1c, where HbA1c is glycated hemoglobin;

[0078] The medication parameter collection unit is used to collect hormone medication parameters, including GCeq, medication cycle and biological agent concentration.

[0079] As a preferred embodiment of the present invention, the monitoring scheme determination module 300 includes:

[0080] An IRI range determination unit is used to determine the range to which the inflammation-insulin resistance index IRI belongs;

[0081] The quarterly monitoring unit is used to determine the monitoring frequency as quarterly monitoring when the IRI is less than the lower limit of the index, and the monitoring items are FBG, CRP and GCeq;

[0082] The biweekly monitoring unit is used to determine the monitoring frequency as biweekly dynamic monitoring when the IRI is between the lower limit and the upper limit of the index. The monitoring items include continuous blood glucose monitoring, hs-CRP and FINS.

[0083] The daily monitoring unit is used to determine the monitoring frequency to be daily when the IRI is greater than the upper limit of the index. The monitoring items are muscle biopsy, joint and pancreatic inflammation imaging.

[0084] As a preferred embodiment of the present invention, the intervention strategy determination module 400 includes:

[0085] A comorbidity status determination unit is used to determine whether a patient is in a critical metabolic-inflammatory comorbidity state based on the inflammation-insulin resistance index and DAS28 score;

[0086] Resistance status determination unit, used to determine whether the patient is in an ultra-high inflammation-driven resistance state based on the inflammation-insulin resistance index and CRP;

[0087] A disorder state determination unit, used to determine whether the patient is in a hormone-induced metabolic disorder state based on GCeq and FBG;

[0088] The intervention strategy generating unit is used to obtain the physiological state according to the determination result and determine the intervention strategy according to the physiological state.

[0089] The above is only a detailed description of the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0090] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0091] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0092] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A method for monitoring and intervening in insulin resistance in rheumatoid diabetes, characterized in that: The method comprises the following steps: Collecting multi-dimensional data information, including rheumatoid arthritis indicators, glucose metabolism parameters, and hormone medication parameters; The inflammation-insulin resistance index (IRI) was constructed as follows: IRI = (FBG × FINS) / correction factor × (1 + α × CRP / 10) × exp(β × GC eq / adjustment parameter), where FBG is fasting blood glucose, FINS is fasting insulin, α and β are constant coefficients, CRP is C-reactive protein, and GCeq is the hormone equivalent dose. Determine monitoring frequency and schedule based on the inflammation-insulin resistance index range; The intervention strategy was determined based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq, and FBG.

2. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, wherein: The correction factor is obtained by fitting experimental data, and the specific steps are: obtaining the actual value of insulin resistance through an insulin clamp test; constructing a regression model, actual insulin resistance = a×(FBG×FINS)+b, and solving for the coefficient a as the correction factor.

3. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, wherein: The adjustment parameters are obtained through clinical trial analysis, and the specific steps are: designing different hormone dosage groups; and determining the adjustment parameters by determining the functional relationship between GCeq and insulin resistance changes through variance analysis or regression analysis.

4. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, wherein: The step of collecting multi-dimensional data information specifically includes: Rheumatoid arthritis indicators were collected, including CRP, DAS28 score and ESR. The DAS28 score is a disease activity score that combines joint tenderness, swelling, and CRP, and the ESR is erythrocyte sedimentation rate. Glucose metabolism parameters were collected, including FBG, FINS, and HbA1c, where HbA1c is glycated hemoglobin; Hormone medication parameters were collected, including GCeq, medication cycle, and biologic concentration.

5. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, wherein: The α is the inflammation regulation coefficient, which is set to 0.35 and is used to reflect the increase in IRI when CRP increases by 10 mg / L; the β is the hormone sensitivity coefficient, which is set to 0.18 and is used to quantify the exponential effect of GCeq when GCeq increases by 5 mg / d.

6. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, wherein: The step of determining the monitoring frequency and monitoring plan based on the inflammation-insulin resistance index range specifically includes: Determine the range of the inflammation-insulin resistance index IRI; When the IRI is less than the lower limit of the index, the monitoring frequency is determined to be quarterly monitoring, and the monitoring items are FBG, CRP and GCeq; When the IRI is between the lower limit and the upper limit of the index, the monitoring frequency is determined to be biweekly dynamic monitoring, and the monitoring items are continuous blood glucose monitoring, hs-CRP, and FINS; When the IRI is greater than the upper limit of the index, the monitoring frequency is determined to be daily, and the monitoring items are muscle biopsy, joint and pancreatic inflammation imaging.

7. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, characterized in that: The step of determining the intervention strategy based on the inflammation-insulin resistance index, DAS28 score, CRP, GCeq and FBG specifically includes: The inflammation-insulin resistance index and DAS28 score were used to determine whether the patient was in a critical metabolic-inflammatory comorbidity state; The inflammation-insulin resistance index and CRP were used to determine whether the patient was in a state of ultra-high inflammation-driven resistance. Determine whether the patient is in a state of hormone-induced metabolic disorder based on GCeq and FBG; The physiological state is obtained according to the determination result, and the intervention strategy is determined according to the physiological state.

8. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 7, characterized in that: The step of determining the intervention strategy according to the physiological state specifically includes: When the physiological state is a critical metabolic-inflammatory comorbidity state, the intervention strategy is: upgrading biological agents and SGLT2 inhibitor combination strategy; When the resistance state is driven by ultra-high inflammation, the intervention strategies are: double plasma exchange, JAK inhibitor switching, and GLP-1 receptor agonist enhancement; When the patient is in a state of hormone-induced metabolic disorder, the intervention strategy is: optimization of metformin sustained-release and dynamic insulin sensitivity compensation.

9. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 7, characterized in that: When IRI>4.5 and DAS28 score>5.1, it is determined to be in a critical metabolic-inflammatory comorbidity state; when CRP>20mg / L and IRI>3.2, it is determined to be in an ultra-high inflammation-driven resistance state; when GCeq>7.5mg / d and FBG fluctuation>3mmol / L, it is determined to be in a hormone-induced metabolic disorder state.

10. The method for monitoring and intervening in insulin resistance in rheumatoid diabetes according to claim 1, characterized in that: The multi-dimensional data information needs to be preprocessed, and the preprocessing includes data standardization, missing value processing and outlier processing.

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